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Physician consultation in young children with recurrent pain-a population-based study.

Hirschfeld G, Wager J, Zernikow B - PeerJ (2015)

Bottom Line: An analysis of the variability of these results indicated that several hundred participants are needed until the results stabilize.Conclusions.On a methodological level, our results show that large-scale studies are need to reliably identify predictors of health care utilization.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Business Management and Social Sciences, University of Applied Sciences Osnabrück , Osnabrück , Germany ; German Paediatric Pain Centre, Children's Hospital Datteln , Germany.

ABSTRACT
Background. Recurrent pain is a common experience in childhood, but only few children with recurrent pain attend a physician. Previous studies yielded conflicting findings with regard to predictors of health care utilization in children with recurrent pain. Methods. The present study analyzes data from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) study comprising n = 2,149 children (3-10 years old) with recurrent pain to find robust predictors. We used multiple logistic regressions to investigate age, gender, socio-economic status (SES), migration background, pain intensity, pain frequency, pain-related disability, mental health problems, and health-related quality of life (HRQL) as predictors for visiting a doctor due to pain. Results. Overall, young girls with high pain-related disability, intensity, frequency, and migration background were more likely to attend a physician. Pain-related disability had the largest impact. Socioeconomic status, health-related quality of life and mental health problems were not systematically related to health care utilization. An analysis of the variability of these results indicated that several hundred participants are needed until the results stabilize. Conclusions. Our findings highlight the importance of pain-related disability and frequency in assessing the severity of recurrent pain. Generic predictors and demographic variables are of lesser relevance to children with recurrent pain. On a methodological level, our results show that large-scale studies are need to reliably identify predictors of health care utilization.

No MeSH data available.


Related in: MedlinePlus

Trajectory of the odds ratios for one sampling order.The trajectory shown summarizes the odds ratios for the different predictors when participants were added one by one to the analysis. The points left points show the ORs for the first 50 participants and the points on the right show the ORs for 2,149 participants. Of note some effects change the direction, e.g., the effect of migration background is smaller than 1 up until about 450 included participants, from which point on the effect becomes larger than 1. Note: Stars denote predictors that were significant in the full sample. Pain-Dis., Pain-related disability; Pain-Int., Pain intensity; Pain-Freq., Pain frequency; HRQL-Psy., Health related quality of life psychological; HRQL-Phys., Health related quality of life physiological; SES, Socioeconomic status; SDQ, Strength and difficulties questionnaire.
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fig-2: Trajectory of the odds ratios for one sampling order.The trajectory shown summarizes the odds ratios for the different predictors when participants were added one by one to the analysis. The points left points show the ORs for the first 50 participants and the points on the right show the ORs for 2,149 participants. Of note some effects change the direction, e.g., the effect of migration background is smaller than 1 up until about 450 included participants, from which point on the effect becomes larger than 1. Note: Stars denote predictors that were significant in the full sample. Pain-Dis., Pain-related disability; Pain-Int., Pain intensity; Pain-Freq., Pain frequency; HRQL-Psy., Health related quality of life psychological; HRQL-Phys., Health related quality of life physiological; SES, Socioeconomic status; SDQ, Strength and difficulties questionnaire.

Mentions: A sequential sampling approach was used to assess the variability of the parameter estimates. The sequential sampling approach tries to illustrate how the ORs of the predictors change when the sample size is gradually increased and identifies a sample size from which on these are stable. Specifically, this entails adding the participants one by one to the dataset and computing the logistic regression with each addition. The resulting sequence of ORs can be plotted against the sample-size showing the trajectory of the ORs. Some ORs for the individual predictors may either be relatively stable across the number of participants—e.g., the OR for pain-intensity in Fig. 2—while others may show some changes depending on the number of participants that were included in the analysis—e.g., the OR for migration in Fig. 2. Of special importance was the point of stability (POS), i.e., the number of participants that had to be included until the significance of this specific effect did not change any more or stabilized (Schönbrodt & Perugini, 2013). Based on the trajectory one can see that at low sample sizes adding participants affects the estimated OR much more than at larger sample sizes. Hence, adding more participants to the analysis before the POS may change not only the magnitude but also whether an effect is significant or not. After the POS, the effect remains stable, i.e., it remains either insignificant or significant. In the example below, the OR for migration was significantly smaller than one when only the first 100 participants were included and significantly larger than one when more than 500 participants were included. The POS for this effect in this specific sampling order was around 500 as from this sample size on adding participants did not change the sign and significance of the effect. Because trajectories and its’ corresponding POS are specific to the particular order in which participants were added to the analysis, we replicated this analysis for 1.000 random orders of participants. For each of the 1.000 random orders, the trajectory and POS were calculated. This resulted in 1,000 slightly different POS for each OR. From the distribution of these POS we calculated the POScrit as the 80th percentile of the POS. This indicates the sample size from which on 80% of the ORs stabilized. Inspection of the POS for 1.000 sequences and POScrit provides an index for how many participants would have to be sampled before the solution stabilizes irrespective of the specific order in which the participants were sampled (Schönbrodt & Perugini, 2013). Data analysis was performed in R. Analysis scripts; the file “Public Use File KiGGS 2003-2006” may be requested at: http://www.rki.de/EN/Content/Health_Monitoring/Public_Use_Files/application/application_node.html will be made available upon request.


Physician consultation in young children with recurrent pain-a population-based study.

Hirschfeld G, Wager J, Zernikow B - PeerJ (2015)

Trajectory of the odds ratios for one sampling order.The trajectory shown summarizes the odds ratios for the different predictors when participants were added one by one to the analysis. The points left points show the ORs for the first 50 participants and the points on the right show the ORs for 2,149 participants. Of note some effects change the direction, e.g., the effect of migration background is smaller than 1 up until about 450 included participants, from which point on the effect becomes larger than 1. Note: Stars denote predictors that were significant in the full sample. Pain-Dis., Pain-related disability; Pain-Int., Pain intensity; Pain-Freq., Pain frequency; HRQL-Psy., Health related quality of life psychological; HRQL-Phys., Health related quality of life physiological; SES, Socioeconomic status; SDQ, Strength and difficulties questionnaire.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4419529&req=5

fig-2: Trajectory of the odds ratios for one sampling order.The trajectory shown summarizes the odds ratios for the different predictors when participants were added one by one to the analysis. The points left points show the ORs for the first 50 participants and the points on the right show the ORs for 2,149 participants. Of note some effects change the direction, e.g., the effect of migration background is smaller than 1 up until about 450 included participants, from which point on the effect becomes larger than 1. Note: Stars denote predictors that were significant in the full sample. Pain-Dis., Pain-related disability; Pain-Int., Pain intensity; Pain-Freq., Pain frequency; HRQL-Psy., Health related quality of life psychological; HRQL-Phys., Health related quality of life physiological; SES, Socioeconomic status; SDQ, Strength and difficulties questionnaire.
Mentions: A sequential sampling approach was used to assess the variability of the parameter estimates. The sequential sampling approach tries to illustrate how the ORs of the predictors change when the sample size is gradually increased and identifies a sample size from which on these are stable. Specifically, this entails adding the participants one by one to the dataset and computing the logistic regression with each addition. The resulting sequence of ORs can be plotted against the sample-size showing the trajectory of the ORs. Some ORs for the individual predictors may either be relatively stable across the number of participants—e.g., the OR for pain-intensity in Fig. 2—while others may show some changes depending on the number of participants that were included in the analysis—e.g., the OR for migration in Fig. 2. Of special importance was the point of stability (POS), i.e., the number of participants that had to be included until the significance of this specific effect did not change any more or stabilized (Schönbrodt & Perugini, 2013). Based on the trajectory one can see that at low sample sizes adding participants affects the estimated OR much more than at larger sample sizes. Hence, adding more participants to the analysis before the POS may change not only the magnitude but also whether an effect is significant or not. After the POS, the effect remains stable, i.e., it remains either insignificant or significant. In the example below, the OR for migration was significantly smaller than one when only the first 100 participants were included and significantly larger than one when more than 500 participants were included. The POS for this effect in this specific sampling order was around 500 as from this sample size on adding participants did not change the sign and significance of the effect. Because trajectories and its’ corresponding POS are specific to the particular order in which participants were added to the analysis, we replicated this analysis for 1.000 random orders of participants. For each of the 1.000 random orders, the trajectory and POS were calculated. This resulted in 1,000 slightly different POS for each OR. From the distribution of these POS we calculated the POScrit as the 80th percentile of the POS. This indicates the sample size from which on 80% of the ORs stabilized. Inspection of the POS for 1.000 sequences and POScrit provides an index for how many participants would have to be sampled before the solution stabilizes irrespective of the specific order in which the participants were sampled (Schönbrodt & Perugini, 2013). Data analysis was performed in R. Analysis scripts; the file “Public Use File KiGGS 2003-2006” may be requested at: http://www.rki.de/EN/Content/Health_Monitoring/Public_Use_Files/application/application_node.html will be made available upon request.

Bottom Line: An analysis of the variability of these results indicated that several hundred participants are needed until the results stabilize.Conclusions.On a methodological level, our results show that large-scale studies are need to reliably identify predictors of health care utilization.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Business Management and Social Sciences, University of Applied Sciences Osnabrück , Osnabrück , Germany ; German Paediatric Pain Centre, Children's Hospital Datteln , Germany.

ABSTRACT
Background. Recurrent pain is a common experience in childhood, but only few children with recurrent pain attend a physician. Previous studies yielded conflicting findings with regard to predictors of health care utilization in children with recurrent pain. Methods. The present study analyzes data from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) study comprising n = 2,149 children (3-10 years old) with recurrent pain to find robust predictors. We used multiple logistic regressions to investigate age, gender, socio-economic status (SES), migration background, pain intensity, pain frequency, pain-related disability, mental health problems, and health-related quality of life (HRQL) as predictors for visiting a doctor due to pain. Results. Overall, young girls with high pain-related disability, intensity, frequency, and migration background were more likely to attend a physician. Pain-related disability had the largest impact. Socioeconomic status, health-related quality of life and mental health problems were not systematically related to health care utilization. An analysis of the variability of these results indicated that several hundred participants are needed until the results stabilize. Conclusions. Our findings highlight the importance of pain-related disability and frequency in assessing the severity of recurrent pain. Generic predictors and demographic variables are of lesser relevance to children with recurrent pain. On a methodological level, our results show that large-scale studies are need to reliably identify predictors of health care utilization.

No MeSH data available.


Related in: MedlinePlus